Civic Technology 2019 Reading

Civic Technology 2019 Reading

Three interesting articles about Crowdsourcing that appeared in late 2019:

My publications about using crowdsourcing in transport are available on my website andynash.com.

2020 Transportation Research Board Annual Meeting

2020 Transportation Research Board Annual Meeting

Two of my papers were accepted for the 2020 TRB Annual Meeting:

Reducing Delays on High-density Railway Lines: Crossrail Case Study

Giorgio Medeossi and Andrew Nash – Monday January 13 – 10:15 AM- 12:00 PM – Convention Center, 144A

The paper describes development of a new timetable designed to reduce delays on the existing Shenfield-London high density regional rail line. The project combined detailed railway operational data with Oyster Card data to identify the root cause of delays and develop timetable improvements. The alternative timetable was tested and refined using stochastic simulation. The new timetable was placed in service during 2016 and led to a significant reduction in delays: punctuality within 5-minutes of scheduled arrival time increased by 6.2% during the most critical hour of the morning peak period.

A Framework for Capturing the Business Benefits of Railway Digitalization

Andrew Nash, Felix Laube and Samuel Roos – Tuesday January 14 – 1:30 PM – 3:15 PM – Convention Center, Hall A

This paper outlines a framework for changing railway systems and processes to help railways capture the full business benefits of digitalization. Economic research shows that businesses need to make fundamental changes to their systems and processes if they are to take full advantage of new technology. The slow implementation of digitally based signaling systems such as ETCS and PTC highlights the need for fundamental change in the railway industry to more aggressively implement new technology – and obtain the full benefits of this technology. The proposed framework integrates an improved and up-to-date understanding of customer needs with a much more efficient and customer-oriented production process. It is designed to make use of today’s powerful data collection, communications and analysis technologies rather than applying new technology to old processes. The proposed framework has been developed based on earlier research results and practical experience. The paper is intended to spur discussion.

Using Open Source Data to Identify Blocked Bus and Bike Lanes

Using Open Source Data to Identify Blocked Bus and Bike Lanes

Exclusive bus lane in Barcelona.

Exclusive bus lane in Barcelona.

Alan Bell has used machine learning to develop a program that analyses data from traffic cameras to identify blocked bus and bike lanes. He analysed a section of St. Nicholas Avenue in Manhattan and found that the bike lane was blocked 55% of the time and the bus stop was blocked 57% of the time between 7am and 7pm.

This is a great example of how people can use open source data to help develop data supporting sustainable transport. In this case it is clear that better enforcement and protected bike lanes are needed. Residents can take this data to government agencies and demand change.

Read more in Bell’s Medium article Drivers Are Breaking the Law, Slowing Commutes and Endangering Lives. I Can Prove It — And Fix It … it includes videos and a link to the program on github.

Transit Alliance Miami – Metrorail Arrival Data

Transit Alliance Miami – Metrorail Arrival Data

Transit Alliance Miami Metrorail frequency dashboard created with open source data.

Transit Alliance Miami Metrorail frequency dashboard created with open source data.

The Transit Alliance Miami has created a simple graphic display illustrating the time between Miami Metrorail trains (frequency) at the Government Center station. They have taken Metrorail data and displayed it in an easy to understand format. It is an excellent example of how city residents can use open data to analyse and publicise the quality of public transport service as part of an advocacy campaign to improve public transport.

According to the website the graphic presents: A real-time audit of Miami’s Metrorail system. It measures the time between each train at Government Center. Each dot represents a train arrival. Every color corresponds to a time. Hover over a dot for more information.

Read more: How Miami Advocates Are Holding Officials Accountable for Transit Performance, by Angie Schmitt, Streeetsblog 25 January 2018.

TRB Annual Meeting 2018

TRB Annual Meeting 2018

I’m co-author for three papers at this year’s Transportation Research Board Annual Meeting in Washington DC (8-13 January 2018). Here’s a list and some links:

18-01196
Feedforward Tactical Optimization for Energy-Efficient Operation of Freight Trains: Swiss Case
Valerio De Martinis, ETHZ – Swiss Federal Institute of Technology
Ambra Toletti, ETHZ – Swiss Federal Institute of Technology
Francesco Corman, ETH Zurich
Ulrich Weidmann, IVT ETH Zürich
Andrew Nash, Emch+Berger AG Bern

18-00903
Application of a Cost-Allocation Model to Swiss Bus and Train lines
Marc Sinner, ETH Zurich
Ulrich Weidmann, IVT ETH Zürich
Andrew Nash, Emch+Berger AG Bern

18-00341
Wireless Electric Propulsion Light Rail Transit Systems in Spain

Francisco Calvo, University of Granada, Spain
Andrew Nash, Emch+Berger AG Bern

2018 Updates

2018 Updates

Screenshot open traffic analyst

Screenshot of Open Traffic Analyst application developed for the World Bank.

Over the holidays I had a chance to update crowdsourced-transport.com with new information. Here are the highlights:

Crowdsourced Public Transport page – added:

Transport Games page – added:

Act! page – added:

Tracking Applications page – added:

  • New category: Open Source Vehicle Tracking with information on Open Traffic platform sponsored by the World Bank.

Crowdsourced Bicycling page – separated:

  • Map-based Reporting (based on GPS tracking) from
  • Pinging Bicycle Data (GPS tracking, plus ability to “ping” en-route to indicate a problem location).
  • DYI Bike Safety – reference to article on making guerrilla bike lanes permanent.
  • Added reference to The hidden bias of big data by Joe Cortright of City Observatory (May 2017) on the need for more cycling data.

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